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Cloud and Big Data Technologies
2,500 words
Cloud and Big Data Technologies – Summative Assessment: Cloud and Big Data Security Application
This summative assessment for the CST4067 Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud and big data security application. The assessment focuses on applying techniques for the complex transformation and processing of data within distributed and cloud-based environments, while considering security, privacy and access-control requirements. Students are expected to develop a practical application and document its design, implementation and evaluation through a technical report and a short demonstration video. Students are given flexibility to select their own project idea, provided that the proposed application is appropriately scoped for the available development period and demonstrates relevant cloud, big data and security technologies. Suggested project areas include data science applications using SQL, MongoDB and cloud storage, privacy-preserving data processing such as Differential Privacy, multi-party authentication, group-based security and access control, Multi-Level Security, Attribute-Based Encryption, and distributed or cloud-based applications incorporating security protocols such as SSH, SSL or IPSEC and access-control mechanisms such as RBAC. The Design and Implementation Document should be no longer than 2,500 words and should explain the major design and implementation aspects of the project. Expected content includes an introduction covering the aims, objectives, project concept, security concepts and cloud technologies used; a requirements specification addressing programme behaviour and security requirements; analysis and design covering protocols, access control and interaction, sequence diagrams or process specifications; implementation details explaining what was achieved and how it was developed; and an evaluation and conclusion discussing successful and unsuccessful aspects, problems encountered and lessons learned. Relevant references, including tutorials, books and academic articles, should also be provided using Harvard or IEEE referencing. The assessment also requires students to submit the implemented Cloud and Big Data Security application together with a highlight demonstration video. The video must be no longer than seven minutes and should demonstrate the main features of the application, including relevant interactions, implementation highlights, security features and, where appropriate, attack scenarios. Assessment is based on the Design and Implementation Document, originality, advanced features, and the effort and quality demonstrated in the application. The assessment specification places particular importance on original development, clear documentation of any tutorials or existing resources used, and evidence that the student understands the technologies implemented. Suggested technologies and project ideas include Google Cloud, Hadoop, Spark, cloud storage, data pipelines, security protocols, access control and privacy-preserving techniques.
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Network Systems and Administration / Cloud Computing / DevOps
2,500 words
Automated Container Deployment and Administration in the Cloud
This practical Network Systems and Administration assessment requires students to design and implement an automated cloud deployment workflow using modern DevOps and infrastructure automation technologies. The overall objective is to deploy a cloud-hosted server running a Docker container while integrating at least two automation tools such as Terraform, AWS CloudFormation, Ansible, Azure DevOps, GitHub Actions or GitHub. Students may use a cloud platform such as AWS, Microsoft Azure or Google Cloud Platform. B9IS121 Network Systems and Adm… The first stage focuses on cloud infrastructure provisioning. Students automate the creation of a server instance, such as an AWS EC2 instance or Azure virtual machine, and configure supporting infrastructure including networking and security groups. Infrastructure must be provisioned through tools such as Terraform, GitHub Actions or AWS CloudFormation. The required outputs include reusable infrastructure scripts or templates and an architecture diagram showing the deployed cloud resources. B9IS121 Network Systems and Adm… The second stage addresses configuration management. Students use Ansible or an equivalent automation platform to configure the provisioned server, install Docker and ensure Docker starts automatically when the server boots. Deliverables include Ansible playbooks or equivalent configuration scripts together with a README file explaining the automation workflow. B9IS121 Network Systems and Adm… The third stage requires students to containerise and deploy a web application. A Dockerfile must be created for either an existing or newly developed sample application, and deployment of the resulting container must be automated on the provisioned server. The required artefacts include the Dockerfile, deployment scripts and application repository. B9IS121 Network Systems and Adm… Students must also create a CI/CD pipeline using Azure DevOps or another suitable CI/CD platform. The pipeline should automatically build and deploy the Docker container whenever new code is pushed to a version-control repository such as GitHub. Students must provide the relevant pipeline configuration, normally in YAML, and demonstrate the pipeline operating successfully. B9IS121 Network Systems and Adm… Documentation is an important part of the assessment. Each student produces an independent PDF report explaining every stage of the automation process, challenges encountered and how those challenges were resolved. A 10-minute walkthrough video must demonstrate the complete end-to-end deployment, and the report must contain a detailed architecture diagram. B9IS121 Network Systems and Adm… The technical report should include a title page, summary, introduction, main technical content, conclusion, references and appropriate appendices. Students are expected to justify the tools selected and critically discuss alternative solutions, advantages and limitations. The report is expected to contain approximately 2,000–2,500 words and should use clear headings, diagrams or flowcharts where relevant, and academic or professional sources such as journal articles, whitepapers, DevOps documentation and industry reports. B9IS121 Network Systems and Adm… B9IS121 Network Systems and Adm… The marking scheme allocates 40% to the demonstration, 50% to report quality and 10% to technical artefacts. Higher-performing submissions are expected to show a working deployment, clear justification of technical actions, strong understanding of automation tools, professional report structure, consistent referencing and reusable scripts that reproduce the same deployment results. B9IS121 Network Systems and Adm… The final submission includes a comprehensive PDF report of approximately 5–8 pages, a detailed README, and links to the relevant scripts, configurations and deployment evidence. B9IS121 Network Systems and Adm…
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Machine Learning / Cloud Computing / Artificial Intelligence
4,004 words
Cloud-Based Machine Learning for Financial Fraud Detection
This Machine Learning on Cloud assessment requires students to design, implement and critically evaluate a cloud-oriented machine-learning solution for financial fraud detection. Working as a group, students address a scenario in which a financial-services organisation requires an automated model capable of detecting fraudulent transactions, reducing financial losses and improving customer security. The project combines machine-learning development with critical evaluation of cloud infrastructure, data preparation, model performance and responsible AI considerations. NUL - LD7187 -Assessment Brief … The project begins with a cloud feasibility study comparing at least two major machine-learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Students evaluate factors including performance, scalability, cost, compliance, system integration and vendor lock-in before providing a justified recommendation. The project then moves into Exploratory Data Analysis, where patterns, anomalies and correlations within the fraud dataset are investigated using visualisations such as heatmaps, histograms and boxplots. NUL - LD7187 -Assessment Brief … A substantial part of the assessment focuses on data preprocessing and class imbalance. Students are expected to clean and transform the dataset, apply scaling and encoding, perform feature engineering and investigate techniques such as SMOTE, undersampling and cost-sensitive learning. These decisions must be justified in terms of their potential effect on predictive performance. NUL - LD7187 -Assessment Brief … Students must then select and train at least two machine-learning models. Suggested algorithms include Logistic Regression, Random Forest, XGBoost and Neural Networks. Appropriate cross-validation and hyperparameter-tuning procedures should be applied, followed by systematic evaluation using precision, recall, F1-score, AUC and precision-recall curves. Supporting visualisations should include confusion matrices, ROC curves and feature-importance analysis. NUL - LD7187 -Assessment Brief … The final component addresses professionalism and ethics in cloud-based AI, including bias, fairness, transparency, data privacy and environmental sustainability. Overall, the project integrates cloud-platform selection, exploratory analytics, preprocessing, imbalanced-data handling, predictive modelling, model evaluation and ethical AI into an applied financial fraud-detection solution. NUL - LD7187 -Assessment Brief … Note: the uploaded brief does not explicitly name a referencing system. If your portal requires a selection, I would use Not specified rather than assume Harvard.
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Machine Learning / Cloud Computing
3,984 words
Machine Learning on Cloud: Fraud Detection Model Design, Training and Evaluation
This postgraduate group project focuses on the design, development and critical evaluation of a cloud-based machine learning solution for financial fraud detection. The scenario involves a financial services organisation seeking to detect fraudulent transactions in order to reduce financial losses and improve customer security. Students are required to analyse an appropriate dataset, develop a machine learning solution, evaluate its effectiveness and critically consider the suitability of cloud technologies for deployment. The project begins with a cloud feasibility study, requiring critical comparison of at least two major machine learning platforms such as Microsoft Azure, Amazon Web Services and Google Cloud Platform. Evaluation criteria include performance, scalability, cost, compliance, integration and vendor lock-in, followed by a justified recommendation. Students then conduct exploratory data analysis to identify patterns, anomalies and correlations using appropriate visualisations such as heatmaps, histograms and boxplots. A substantial component addresses data preprocessing and class imbalance. Students are expected to clean and transform the data, apply scaling and encoding, perform feature engineering and investigate approaches such as SMOTE, undersampling and cost-sensitive learning. Each preprocessing choice must be justified in terms of its potential impact on model performance. Students must select and train at least two machine learning models, with suggested approaches including Logistic Regression, Random Forest, XGBoost and Neural Networks. Model development incorporates cross-validation and hyperparameter tuning. Evaluation uses fraud-relevant measures including Precision, Recall, F1 score, AUC and precision-recall curves, supported by confusion matrices, ROC curves and feature-importance visualisations. The project concludes with critical consideration of professional and ethical issues in cloud-based AI, including bias, fairness, transparency, data privacy and sustainability. The overall assessment therefore integrates cloud-platform evaluation, machine learning development, imbalanced classification, model evaluation and responsible AI practice. Overview word count: approximately 340 words.
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Cloud Computing / Big Data Technologies / Cyber Security
2,500 words
Cloud and Big Data Security Application: Design, Implementation and Evaluation
This assessment for the Cloud and Big Data Technologies module requires students to design, implement and evaluate an individual cloud-based or distributed data application. The project focuses on practical solutions involving the complex transformation, processing, storage and security of big data within cloud environments. Students are expected to demonstrate how distributed data can be organised in the cloud, how data pipelines can be used to access or process distributed databases, and how appropriate security controls can be incorporated into the resulting architecture. Students have considerable freedom when selecting their application. Suggested project directions include developing a data-science solution using SQL or MongoDB with cloud storage and an appropriate security policy; implementing privacy-preserving distributed processing using techniques such as Differential Privacy; creating multi-party authentication and group-based access-control mechanisms; or designing Multi-Level Security, Attribute-Based Encryption or Role-Based Access Control solutions. Projects may also examine distributed or cloud applications using security protocols such as SSH, SSL or IPsec. Creativity and originality are explicitly encouraged. The written component is a Design and Implementation Document of no more than approximately 2,500 words. It should present the project aims and objectives, application concept, cloud and security technologies, functional and security requirements, architecture and design decisions, protocols, access-control mechanisms, implementation process, achievements, problems encountered and overall evaluation. Relevant diagrams, such as interaction or sequence diagrams, may be used to explain system behaviour and architecture. The assessment also requires submission of the functioning Cloud and Big Data Security application and a 7-minute highlight demonstration video. The video should demonstrate the application's major features, implementation details, security functionality and, where appropriate, attack scenarios. Assessment places strong emphasis on the quality of the design and implementation documentation, originality, use of advanced features, and the overall effort and technical quality of the completed application. Students are therefore expected to demonstrate independent development rather than simply reproduce an existing tutorial.
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